Protein function classification via support vector machine approach

被引:116
作者
Cai, CZ
Wang, WL
Sun, LZ
Chen, YZ [1 ]
机构
[1] Chongqing Univ, Dept Appl Phys, Chongqing 400044, Peoples R China
[2] Natl Univ Singapore, Dept Computat Sci, Singapore 117543, Singapore
关键词
support vector machine; classification; RNA-binding protein; protein homodimer; drug absorption protein; drug distribution protein; drug metabolizing enzyme; drug excretion protein;
D O I
10.1016/S0025-5564(03)00096-8
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
Support vector machine (SVM) is introduced as a method for the classification of proteins into functionally distinguished classes. Studies are conducted on a number of protein classes including RNA-binding proteins; protein homodimers, proteins responsible for drug absorption, proteins involved in drug distribution and excretion, and drug metabolizing enzymes. Testing accuracy for the classification of these protein classes is found to be in the range of 84-96%. This suggests the usefulness of SVM in the classification of protein functional classes and its potential application in protein function prediction. (C) 2003 Elsevier Inc. All rights reserved.
引用
收藏
页码:111 / 122
页数:12
相关论文
共 46 条
  • [1] [Anonymous], 1999, The Nature Statist. Learn. Theory
  • [2] Assessing the accuracy of prediction algorithms for classification: an overview
    Baldi, P
    Brunak, S
    Chauvin, Y
    Andersen, CAF
    Nielsen, H
    [J]. BIOINFORMATICS, 2000, 16 (05) : 412 - 424
  • [3] Identifying genes related to drug anticancer mechanisms using support vector machine
    Bao, L
    Sun, ZR
    [J]. FEBS LETTERS, 2002, 521 (1-3) : 109 - 114
  • [4] Fusion of face and speech data for person identity verification
    Ben-Yacoub, S
    Abdeljaoued, Y
    Mayoraz, E
    [J]. IEEE TRANSACTIONS ON NEURAL NETWORKS, 1999, 10 (05): : 1065 - 1074
  • [5] Predicting protein-protein interactions from primary structure
    Bock, JR
    Gough, DA
    [J]. BIOINFORMATICS, 2001, 17 (05) : 455 - 460
  • [6] Knowledge-based analysis of microarray gene expression data by using support vector machines
    Brown, MPS
    Grundy, WN
    Lin, D
    Cristianini, N
    Sugnet, CW
    Furey, TS
    Ares, M
    Haussler, D
    [J]. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 2000, 97 (01) : 262 - 267
  • [7] Drug design by machine learning: support vector machines for pharmaceutical data analysis
    Burbidge, R
    Trotter, M
    Buxton, B
    Holden, S
    [J]. COMPUTERS & CHEMISTRY, 2001, 26 (01): : 5 - 14
  • [8] A tutorial on Support Vector Machines for pattern recognition
    Burges, CJC
    [J]. DATA MINING AND KNOWLEDGE DISCOVERY, 1998, 2 (02) : 121 - 167
  • [9] CAI CZ, 2003, IN PRESS INT J MOD C, V14
  • [10] Prediction of protein structural classes by support vector machines
    Cai, YD
    Liu, XJ
    Xu, XB
    Chou, KC
    [J]. COMPUTERS & CHEMISTRY, 2002, 26 (03): : 293 - 296